


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__  	=     logging.get_logger(__name__)

__magic_name__  	=     '''▁'''

__magic_name__  	=     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

__magic_name__  	=     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

__magic_name__  	=     {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

__magic_name__  	=     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

__magic_name__  	=     {'''mustc''': MUSTC_LANGS}





class        lowerCAmelCase__ (					__lowerCamelCase  ):






						"""simple docstring"""
						__UpperCAmelCase       :						Optional[int]    	=			VOCAB_FILES_NAMES
						__UpperCAmelCase       :						str    	=			PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase       :						Union[str, Any]    	=			MAX_MODEL_INPUT_SIZES
						__UpperCAmelCase       :						Union[str, Any]    	=			['''input_ids''', '''attention_mask''']

						__UpperCAmelCase       :						List[Any]    	=			[]




						def __init__(       self ,				a_ ,				a_ ,				a_="<s>" ,				a_="</s>" ,				a_="<pad>" ,				a_="<unk>" ,				a_=False ,				a_=False ,				a_=None ,				a_=None ,				a_ = None ,				**a_ ,				):
							lowerCamelCase_					:				Any              =  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=a_ ,				eos_token=a_ ,				unk_token=a_ ,				pad_token=a_ ,				do_upper_case=a_ ,				do_lower_case=a_ ,				tgt_lang=a_ ,				lang_codes=a_ ,				sp_model_kwargs=self.sp_model_kwargs ,				**a_ ,				)
							lowerCamelCase_					:				Optional[int]              =  do_upper_case
							lowerCamelCase_					:				List[Any]              =  do_lower_case

							lowerCamelCase_					:				List[Any]              =  load_json(a_      )
							lowerCamelCase_					:				Union[str, Any]              =  {v: k for k, v in self.encoder.items()}
							lowerCamelCase_					:				Optional[int]              =  spm_file
							lowerCamelCase_					:				List[Any]              =  load_spm(a_ ,				self.sp_model_kwargs      )

							if lang_codes is not None:
								lowerCamelCase_					:				Dict              =  lang_codes
								lowerCamelCase_					:				Union[str, Any]              =  LANGUAGES[lang_codes]
								lowerCamelCase_					:				Any              =  [F"""<lang:{lang}>""" for lang in self.langs]
								lowerCamelCase_					:				Tuple              =  {lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""      ) for lang in self.langs}

								lowerCamelCase_					:				Union[str, Any]              =  self.lang_tokens
								lowerCamelCase_					:				int              =  tgt_lang if tgt_lang is not None else self.langs[0]

								self.set_tgt_lang_special_tokens(self._tgt_lang      )
							else:
								lowerCamelCase_					:				Union[str, Any]              =  {}




						@property
						def 	_UpperCamelCase					(       self      ):
							return len(self.encoder      )




						@property
						def 	_UpperCamelCase					(       self      ):
							return self._tgt_lang




						@tgt_lang.setter
						def 	_UpperCamelCase					(       self ,				a_      ):
							lowerCamelCase_					:				List[str]              =  new_tgt_lang
							self.set_tgt_lang_special_tokens(a_      )




						def 	_UpperCamelCase					(       self ,				a_      ):
							lowerCamelCase_					:				List[Any]              =  self.lang_code_to_id[tgt_lang]
							lowerCamelCase_					:				List[str]              =  [lang_code_id]




						def 	_UpperCamelCase					(       self ,				a_      ):
							return self.sp_model.encode(a_ ,				out_type=a_      )




						def 	_UpperCamelCase					(       self ,				a_      ):
							return self.encoder.get(a_ ,				self.encoder[self.unk_token]      )




						def 	_UpperCamelCase					(       self ,				a_      ):
							return self.decoder.get(a_ ,				self.unk_token      )




						def 	_UpperCamelCase					(       self ,				a_      ):
							lowerCamelCase_					:				int              =  []
							lowerCamelCase_					:				Dict              =  ""
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									lowerCamelCase_					:				Any              =  self.sp_model.decode(a_      )
									out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
									lowerCamelCase_					:				int              =  []
								else:
									current_sub_tokens.append(a_      )
							lowerCamelCase_					:				str              =  self.sp_model.decode(a_      )
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()




						def 	_UpperCamelCase					(       self ,				a_ ,				a_=None      ):
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ = None ,				a_ = False      ):

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=a_ ,				token_ids_a=a_ ,				already_has_special_tokens=a_      )

							lowerCamelCase_					:				List[Any]              =  [1] * len(self.prefix_tokens      )
							lowerCamelCase_					:				List[str]              =  [1]
							if token_ids_a is None:
								return prefix_ones + ([0] * len(a_      )) + suffix_ones
							return prefix_ones + ([0] * len(a_      )) + ([0] * len(a_      )) + suffix_ones




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Dict              =  self.encoder.copy()
							vocab.update(self.added_tokens_encoder      )
							return vocab




						def __getstate__(       self      ):
							lowerCamelCase_					:				List[Any]              =  self.__dict__.copy()
							lowerCamelCase_					:				int              =  None
							return state




						def __setstate__(       self ,				a_      ):
							lowerCamelCase_					:				str              =  d

							# for backward compatibility
							if not hasattr(self ,				"sp_model_kwargs"      ):
								lowerCamelCase_					:				Union[str, Any]              =  {}

							lowerCamelCase_					:				List[Any]              =  load_spm(self.spm_file ,				self.sp_model_kwargs      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ = None      ):
							lowerCamelCase_					:				Optional[Any]              =  Path(a_      )
							assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
							lowerCamelCase_					:				str              =  save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
							)
							lowerCamelCase_					:				List[Any]              =  save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
							)

							save_json(self.encoder ,				a_      )

							if os.path.abspath(self.spm_file      ) != os.path.abspath(a_      ) and os.path.isfile(self.spm_file      ):
								copyfile(self.spm_file ,				a_      )
							elif not os.path.isfile(self.spm_file      ):
								with open(a_ ,				"wb"      ) as fi:
									lowerCamelCase_					:				List[str]              =  self.sp_model.serialized_model_proto()
									fi.write(a_      )

							return (str(a_      ), str(a_      ))







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				str              =  sentencepiece.SentencePieceProcessor(**a_)
	spm.Load(str(a_))
	return spm







def 							__magic_name__    (     lowerCAmelCase_):
	'''simple docstring'''


	with open(a_     ,	"r") as f:
		return json.load(a_)







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	with open(a_     ,	"w") as f:
		json.dump(a_     ,	a_     ,	indent=2)







'''simple docstring'''


import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """segformer"""







   def __init__(						self   ,				UpperCamelCase=3   ,				UpperCamelCase=4   ,				UpperCamelCase=[2, 2, 2, 2]   ,				UpperCamelCase=[8, 4, 2, 1]   ,				UpperCamelCase=[32, 64, 160, 256]   ,				UpperCamelCase=[7, 3, 3, 3]   ,				UpperCamelCase=[4, 2, 2, 2]   ,				UpperCamelCase=[1, 2, 5, 8]   ,				UpperCamelCase=[4, 4, 4, 4]   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.02   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-6   ,				UpperCamelCase=256   ,				UpperCamelCase=255   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(**UpperCamelCase   )

     if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
           ' removed, as the behaviour will default to that of reshape_last_stage = True.'   ,				UpperCamelCase   ,				)

     __a								=       num_channels
     __a								=       num_encoder_blocks
     __a								=       depths
     __a								=       sr_ratios
     __a								=       hidden_sizes
     __a								=       patch_sizes
     __a								=       strides
     __a								=       mlp_ratios
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       classifier_dropout_prob
     __a								=       initializer_range
     __a								=       drop_path_rate
     __a								=       layer_norm_eps
     __a								=       decoder_hidden_size
     __a								=       kwargs.get('reshape_last_stage'   ,				UpperCamelCase   )
     __a								=       semantic_loss_ignore_index





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-4







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12



